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Establishment of structural and functional correspondences of human brain that can be quantitatively encoded and reproduced across different subjects and populations is one of the key issues in brain mapping. As an attempt to address this challenge, our recently developed Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOL) system reported 358 connectional landmarks, each of which possesses consistent DTI-derived white matter fiber connection pattern that is reproducible in over 240 healthy brains. However, the DICCCOL system can be substantially improved by integrating anatomical and morphological information during landmark initialization and optimization procedures. In this paper, we present a novel anatomy-guided landmark discovery framework that defines and optimizes landmarks via integrating rich anatomical, morphological, and fiber connectional information for landmark initialization, group-wise optimization and prediction, which are formulated and solved as an energy minimization problem. The framework finally determined 555 consistent connectional landmarks. Validation studies demonstrated that the 555 landmarks are reproducible, predictable, and exhibited reasonably accurate anatomical, connectional, and functional correspondences across individuals and populations and thus are named anatomy-guided DICCCOL or A-DICCCOL. This A-DICCCOL system represents common cortical architectures with anatomical, connectional, and functional correspondences across different subjects and would potentially provide opportunities for various applications in brain science. [ABSTRACT FROM AUTHOR]

For decades, it has been largely unknown to what extent multiple functional networks spatially overlap/interact with each other and jointly realize the total cortical function. Here, by developing novel sparse representation of whole-brain fMRI signals and by using the recently publicly released large-scale Human Connectome Project high-quality fMRI data, we show that a number of reproducible and robust functional networks, including both task-evoked and resting state networks, are simultaneously distributed in distant neuroanatomic areas and substantially spatially overlapping with each other, thus forming an initial collection of holistic atlases of functional networks and interactions (HAFNIs). More interestingly, the HAFNIs revealed two distinct patterns of highly overlapped regions and highly specialized regions and exhibited that these two patterns of areas are reciprocally localized, revealing a novel organizational principle of cortical function. [ABSTRACT FROM AUTHOR]

The relationship between brain structure and function has been one of the centers of research in neuroimaging for decades. In recent years, diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) techniques have been widely available and popular in cognitive and clinical neurosciences for examining the brain's white matter (WM) micro-structures and gray matter (GM) functions, respectively. Given the intrinsic integration of WM/GM and the complementary information embedded in DTI/fMRI data, it is natural and well-justified to combine these two neuroimaging modalities together to investigate brain structure and function and their relationships simultaneously. In the past decade, there have been remarkable achievements of DTI/fMRI fusion methods and applications in neuroimaging and human brain mapping community. This survey paper aims to review recent advancements on methodologies and applications in incorporating multimodal DTI and fMRI data, and offer our perspectives on future research directions. We envision that effective fusion of DTI/fMRI techniques will play increasingly important roles in neuroimaging and brain sciences in the years to come. [ABSTRACT FROM AUTHOR]

Diffusion tensor imaging (DTI) and high angular resolution diffusion imaging (HARDI) have been broadly used in the neuroimaging field to investigate the macro-scale fiber connection patterns in the cerebral cortex. Our recent analyses of DTI and HARDI data demonstrated that gyri are connected by denser, streamlined fibers than sulci are. Inspired by this finding and motivated by the fact that DTI-derived fibers provide the structural substrates for functional connectivity, we hypothesize that gyri are global functional connection centers and sulci are local functional units. To test this functional model of gyri and sulci, we examined the structural and functional connectivity among the landmarks on the selected gyral/sulcal areas in the frontal/parietal lobe and in the whole cerebral cortex via multimodal DTI and resting state fMRI (R-fMRI) datasets. Our results demonstrate that functional connectivity is strong among gyri, weak among sulci, and moderate between gyri and sulci. These results suggest that gyri are functional connection centers that exchange information among remote structurally connected gyri and neighboring sulci, while sulci communicate directly with their neighboring gyri and indirectly with other cortical regions through gyri. This functional model of gyri and sulci has been supported by a series of experiments, and provides novel perspectives on the functional architecture of the cerebral cortex. [ABSTRACT FROM AUTHOR]

Quantitative modeling and analysis of structural and functional brain networks based on diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) data have received extensive interest recently. However, the regularity of these structural and functional brain networks across multiple neuroimaging modalities and also across different individuals is largely unknown. This paper presents a novel approach to inferring group-wise consistent brain subnetworks from multimodal DTI/resting-state fMRI datasets via multi-view spectral clustering of cortical networks, which were constructed upon our recently developed and validated large-scale cortical landmarks—DICCCOL (dense individualized and common connectivity-based cortical landmarks). We applied the algorithms on DTI data of 100 healthy young females and 50 healthy young males, obtained consistent multimodal brain networks within and across multiple groups, and further examined the functional roles of these networks. Our experimental results demonstrated that the derived brain networks have substantially improved inter-modality and inter-subject consistency. [ABSTRACT FROM PUBLISHER]

The highly convoluted cerebral cortex is characterized by two different topographic structures: convex gyri and concave sulci. Increasing studies have demonstrated that cortical gyri and sulci exhibit different structural connectivity patterns. Inspired by the intrinsic structural differences between gyri and sulci, in this paper, we present a data-driven framework based on sparse representation of fMRI data for functional network inferences, then examine the interactions within and across gyral and sulcal functional networks and finally elucidate possible functional differences using graph theory based properties. We apply the proposed framework to the high-resolution Human Connectome Project (HCP) grayordinate fMRI data. Extensive experimental results on both resting state fMRI data and task-based fMRI data consistently suggested that gyri are more functionally integrated, while sulci are more functionally segregated in the organizational architecture of cerebral cortex, offering novel understanding of the byzantine cerebral cortex. [ABSTRACT FROM AUTHOR]

There have been several recent studies that used sparse representation for fMRI signal analysis and activation detection based on the assumption that each voxel’s fMRI signal is linearly composed of sparse components. Previous studies have employed sparse coding to model functional networks in various modalities and scales. These prior contributions inspired the exploration of whether/how sparse representation can be used to identify functional networks in a voxel-wise way and on the whole brain scale. This paper presents a novel, alternative methodology of identifying multiple functional networks via sparse representation of whole-brain task-based fMRI signals. Our basic idea is that all fMRI signals within the whole brain of one subject are aggregated into a big data matrix, which is then factorized into an over-complete dictionary basis matrix and a reference weight matrix via an effective online dictionary learning algorithm. Our extensive experimental results have shown that this novel methodology can uncover multiple functional networks that can be well characterized and interpreted in spatial, temporal and frequency domains based on current brain science knowledge. Importantly, these well-characterized functional network components are quite reproducible in different brains. In general, our methods offer a novel, effective and unified solution to multiple fMRI data analysis tasks including activation detection, de-activation detection, and functional network identification. [ABSTRACT FROM AUTHOR]

Highlights: [•] Representation of structural connectomes by DICCCOL based on DTI data. [•] Construction of whole-brain quasi-stable functional connectomes from fMRI data. [•] Learn atomic connectome patterns (ACPs) via effective sparse representations. [•] Characterization of task-performance and task-free states by ACPs. [•] Novel insights into the functional architectures of the brain. [Copyright &y& Elsevier]